Distinctive Analytics

The Matplotlib library helps you create static and dynamic visualizations. Dynamic visualizations that are animated and interactive. This library makes it easy to plot data and create graphs.

install and Import Library

import matplotlib

# install
Install.packages("ggplot2")

# Load package
library("ggplot2")

Import specific method from matplotlib

import matplotlib.pyplot as plt

Line plot

Let’s use the plot function, for which we provide two columns from datasets.

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import matplotlib.pyplot as plt
# import data preparation library
import numpy as np
# data handling library
import pandas as pd


bill=[25,30,40,50,70,85,95]
tips=[4,6,6.5,7,8.5,9.3,10.8]

print(len(bill),len(tips))
plt.plot(bill,tips)
plt.title('Bill To Tip Ratio')
plt.ylabel('Tips')
plt.xlabel('Bills')

# Load package
library("ggplot2")

df<- data.frame(bill=c(25,30,40,50,70,85,95),tips=(4,6,6.5,7,8.5,9.3,10.8))

library(ggplot2)
# Line plot
ggplot(data=df, aes(x=bill, y=tips, group=1)) + geom_line() + geom_point()

Bar Plot

Bar plot represent data similar to rectangular bars. That are Normalized by a factor to be represented in a graph. Each bar represents a individual value or variable provided.

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import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import numpy as np

figure,axes=plt.subplots(1,1)
employees=np.array([20,50,8,100])
axes.hist(employees,bins='auto')
axes.set_title('Histogram')
axes.set_xlabel('Count')
axes.set_ylabel('Employees')
plt.show()

library("ggplot2")
df<-data.frame(Department=c('Finance','Manager','Executive'),Employees=c(20,50,8))
ggplot(data=df,aes(x=Department,y=Employees))+geom_bar(stat='identity')

Histogram

Histograms are similar to graphs. They group variables or classes to represent data in a rectangular bar. These bars are also called as bin.

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import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import numpy as np

figure,axes=plt.subplots(1,1)
employees=np.array([20,50,8,100])
axes.hist(employees,bins='auto')
axes.set_title('Histogram')
axes.set_xlabel('Count')
axes.set_ylabel('Employees')
plt.show()

library("ggplot2")
df<-c(20,50,8,100)
hist(df,xlab="Employee count",col="green",border="black")

Scatter plot

Scatter plot points out the exact placement of a data point. Using scatter plots, we can find clustered data. We can also use a third variable to change the size of the circle. It will add a third dimension to the graph.

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import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import numpy as np

Department=['Finance','Manager','Executive','Worker']
employees=[20,50,8,100]
salary=[75,165,469,25]
figure=plt.figure()
axes=figure.add_axes([0,0,1,1])

axes.scatter(Department,employees,color='b')
axes.set_xlabel('Department')
axes.set_ylabel('Employees')
axes.set_title('Scatter_plot')
plt.show()

library("ggplot2")
df<-data.frame(Department=c('Finance','Manager','Executive','Worker'),Employees=c(20,50,8,100),salary=c(75,165,469,25))
ggplot(df,aes(x=Department,y=Employees))+geom_point(size=2,shape=46)

In the following code, we used the third variable, salary, to differentiate data points from each other.

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import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import numpy as np

Department=['Finance','Manager','Executive','Worker']
employees=[20,50,8,100]
salary=[75,165,469,25]
figure=plt.figure()
axes=figure.add_axes([0,0,1,1])

axes.scatter(Department,employees,salary,color='b')
axes.set_xlabel('Department')
axes.set_ylabel('Employees')
axes.set_title('Scatter_plot')
plt.show()

library("ggplot2")
df<-data.frame(Department=c('Finance','Manager','Executive'),Employees=c(20,50,8),salary=c(75,165,469,25))


ggplot(df, aes(x=Employees, y=salary, shape=salary, color=Department)) +
  geom_point()

Sub Plots

There is a concept of subplots in Matplotlib where we can create a grid of plots. The grid can organise plots.

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import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import numpy as np

Department=['Finance','Manager','Executive','Worker']
employees=[20,50,8,100]
salary=[75,165,469,25]

plt.subplot(1,2,2)

plt.bar(Department,salary,width=0.5)
plt.xlabel("Department")
plt.ylabel("Salary")

plt.subplot(1,2,2)
figure=plt.figure()
axes=figure.add_axes([0,0,1,1])

axes.scatter(Department,employees,salary,color='b')
axes.set_xlabel('Department')
axes.set_ylabel('Employees')
axes.set_title('Scatter_plot')

plt.show()

library("ggplot2")
df<-data.frame(Department=c('Finance','Manager','Executive','Worker'),Employees=c(20,50,8,100),salary=c(75,165,469,25))

ggplot(data=df,aes(x=Department,y=Employees))+geom_bar(stat='identity')
ggplot(df, aes(x=Employees, y=salary, shape=salary, color=Department))+geom_point()

Time Series Data Plot

We have saved the stock data in the nested dictionaries. Here we plot accurate data points with plt.plot() and plt.scatter(). By using these, we create connected scatter plots. Using the following method, you can mix multiple types of plots into a single graph.

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# load libraries
import matplotlib.pyplot as plt
from matplotlib import style
import pandas as pd
import numpy as np

# create dataset
Stocks=pd.DataFrame({'Year':[2022,2021,2020,2019,2018],'Motors_Turnover':[47263.68,47031.47,43928.17,69202.76,58831.41],'Steel_Turnover':[129021.35,64869.00,60435.97,70610.92,59160.79]})

# plot multiple lines in one plot
plt.plot(Stocks['Year'],Stocks['Motors_Turnover'],label="Tata Motors",color='r')
plt.scatter(Stocks['Year'],Stocks['Motors_Turnover'], color='r')
plt.plot(Stocks['Year'],Stocks['Steel_Turnover'],label="Steel Motors",color='b')
plt.scatter(Stocks['Year'],Stocks['Steel_Turnover'],color='b')
plt.legend()
plt.xlabel('Years')
plt.ylabel('Net TurnOver (Crores)')
plt.title('Information')
plt.show()

# Load library
library("ggplot2") 
# load dataset 
stock<-data.frame(Year=c(2022,2021,2020,2019,2018),Motors_Turnover=c(47263.68,47031.47,43928.17,69202.76,58831.410),Steel_Turnover=c(129021.35,64869.00,60435.97,70610.92,59160.79))
# Save two plots in stock_plot 
stock_plot<- ggplot(stock, aes(Year))+geom_line(aes(y=Motors_Turnover),color = "green") + geom_line(aes(y = Steel_Turnover), color = "blue")
stock_plot